1357129 - Novel Reconstruction Method to Improve Sharpness of Free-Breathing Cardiac MR Late Gadolinium Enhancement Images on a commercial 0.55T System

Low-field CMR is potentially a cost-effective alternative to high-field. However, the intrinsically low signal-to-noise ratio (SNR) can degrade image quality, especially in late gadolinium enhancement (LGE) [1]. To improve SNR, the well-established motion correction (MOCO)+averaging method [2,3] can be applied. However, low SNR single-shot source images can make MOCO challenging at low field, the need for additional averaging can introduce blurring, and higher acceleration is needed to overcome limited gradient performance (26 mT/m, 45 mT/m/ms).

In this study, we propose a novel MOCO compressed sensing (CS) method (MOCO+CS) to improve LGE image sharpness by incorporating MOCO into a CS reconstruction algorithm.

Methods:

Multiple single-shot LGE images were acquired in 12 healthy volunteers on a commercial 0.55T MR scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen, Germany). The k-space data from these scans was used to develop and test the effectiveness of the proposed MOCO+CS method.

40 sets of k-space raw data from LGE image series were retrospectively included, comprising 29 short axis, 9 four-chamber long axis, and 2 two-chamber image series. A uniformly down-sampled acceleration rate = 2, TSENSE/TGRAPPA acquisition pattern was used.      

All k-space data sets were reconstructed using both the MOCO+averaging method that employs a TGRAPPA reconstruction of the source images prior to MOCO, and the proposed MOCO+CS method. MOCO+CS incorporated the estimated motion displacement field into the CS cost function, i.e.:

argmin_x||DFSRx - k₀||₂ + ρ||TVx||₁

where x is the motion corrected averaged image; k₀ is the acquired k-space raw data; R is the forward MOCO operator; S is the sensitivity map; F is the Fourier transform operator; D is the down-sampling operator; TV is the spatial total variation operator; ρ is an adjustable parameter. The “demon” MOCO algorithm was employed [4].

Boundary (edge) sharpness was evaluated in each pixel at the boundary between the LV myocardium and blood pool using the sigmoid function fitting method along the normal direction [5]. A paired t-test was used to evaluate the significance of the boundary sharpness differences.

Results: Figure 1 shows a typical image reconstructed by both methods. The image reconstructed using the proposed MOCO+CS method has visibly improved sharpness. The mean image sharpness of MOCO+averaging and MOCO+CS were 0.83+/-0.17 pixel^-1 and 1.01+/- 0.24 pixel^-1(p-value < 0.001), respectively. The image sharpness improvement is shown in Figure 2.

Conclusion: This study demonstrates that MOCO+CS improves the sharpness of free-breathing LGE images acquired using a 0.55T commercial scanner. We believe the loss of sharpness is due to the residual motion in corrected images, and the need for more averages to boost SNR.  The proposed method leverages all available raw data to reconstruct a final image, effectively incorporating the SNR boost of averaging into the CS reconstruction, while motion correcting to avoid blurring.